Generative Models

One-step Latent-free Image Generation with Pixel Mean Flows
https://arxiv.org/abs/2601.22158

Implementation of rectified flow and some of its followup research / improvements in Pytorch
https://github.com/lucidrains/rectified-flow-pytorch

TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows
https://github.com/inclusionAI/TwinFlow

Improved Mean Flows: On the Challenges of Fastforward Generative Models
https://arxiv.org/abs/2512.02012

Just image Transformer (JiT) for Pixel-space Diffusion
https://github.com/LTH14/JiT

pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
https://github.com/Lakonik/piFlow

FARMER: Flow AutoRegressive Transformer over Pixels
https://huggingface.co/papers/2510.23588

REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers
https://github.com/End2End-Diffusion/REPA-E

Calibrating Generative Models
https://github.com/smithhenryd/cgm

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
https://arxiv.org/abs/2510.01329

Diffusion Transformers with Representation Autoencoders
https://github.com/bytetriper/RAE

DiffuseNNX library for diffusion and flow matching generative algorithms
https://github.com/willisma/diffuse_nnx

Learn to Guide Your Diffusion Model
https://arxiv.org/abs/2510.00815

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
https://arxiv.org/abs/2506.06276

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
https://arxiv.org/abs/2509.03726

Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models
https://arxiv.org/abs/2510.02300

A Theoretical Analysis of Discrete Flow Matching Generative Models
https://arxiv.org/abs/2509.22623

TinyWorlds is a minimal autoregressive world model built on Google Deepmind's Genie Architecture
https://github.com/AlmondGod/tinyworlds